Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation
Last commit November 1, 2026, 12 days ago.
943 GitHub stars and 0 Honeystax upvotes. Copy the install, test the workflow, then decide if it earns a permanent slot.
You can test this quickly and remove it cleanly if it misses.
Starts at 6. Added: GitHub health not measured +10, no security policy +8, no contributing guide +4, no code of conduct +3.
AI Agent
Universal
Model
Multiple
Fastest way to find out if retentioneering-tools belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
git clone https://github.com/retentioneering/retentioneering-tools ~/.claude/skills/retentioneering-toolsRun this first. You will know quickly if the workflow earns a permanent slot.
rm -rf ~/.claude/skills/retentioneering-toolsNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude/ ├─ commands/ ├─ skills/ │ └─ retentioneering-tools/ ← installs here ├─ agents/ └─ settings.json
retentioneering-tools is listed on Honeystax as a Skill for Universal, since October 7, 2026. Its code is at github.com/retentioneering/retentioneering-tools, with 943 GitHub stars. Last commit November 1, 2026, 12 days ago.
Source: GitHub repository
Source check: November 1, 2026
Upstream commit: November 1, 2026
Repository state: Not marked archived
Honeystax upvotes are community interest signals, not star ratings. GitHub stars and repository health are source measurements; editorial risk and trial-cost notes are Honeystax analysis.